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arXiv · 2604.25972

A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication

Valentin Cuzin-Rambaud·Laetitia Matignon·Maxime Morge
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Abstract

In multi-agent reinforcement learning (MARL), the integration of a communication mechanism, allowing agents to better learn to coordinate their actions and converge on their objectives by sharing information. Based on an interaction graph, a subclass of methods employs graph neural networks (GNNs) to learn the communication, enabling agents to improve their internal representations by enriching them with information exchanged. With growing research, we note a lack of explicit structure and framework to distinguish and classify MARL approaches with communication based on GNNs. Thus, this paper surveys recent works in this field. We propose a generalized GNN-based communication process with the goal of making the underlying concepts behind the methods more obvious and accessible.

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Valentin Cuzin-Rambaud, Laetitia Matignon, Maxime Morge. 2026-04-28. A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication. https://arxiv.org/abs/2604.25972

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